The use of optics in computing systems holds a great deal of promise. The high speed and three-dimensional nature of optics allows one to solve the connectivity and input/output limitations of electronics systems. However, a major limitation in the implementation of an all-optical computing system is the lack of generally useful non-linear optical devices (i.e., the analog of the electronic transistor). As in the case of electronic computers, the presence of a device like the transistor allows one to implement systems using a building block concept. This results in highly flexible technology with standardized design and verification capabilities. Typically, electronic systems are limited by the amount of connectivity that can be supported in the area of the chip and pinout limitations in the connections between chips. This, in turn, limits the degree of parallelism which can be implemented in an all-electronic system. By integrating electronic computation with optical connectivity, the advantages of each technology can compensate for the limitation of the other.
The success of such a hybrid approach is critically dependant on the choice of the problem that needs to be solved. Specifically, it is necessary that the problem require a high degree of parallelism. Image processing is a prime example of such a problem because the input signal itself often consists of a very large number of pixels that can be processed in parallel. The operations that are typically performed in order to recognize the image consists of relatively simple pre-processing steps that extract features, such as edges, depth, parallax, etc. Such operations are generally local, shift invariant operations that can be implemented in a straight forward way electronically. The difficulty in the electronic implementation of these pre-processing steps are the bottlenecks that exist in communicating images/data in and out of the chips that compute each of these operations. The opto-electronic method of the present invention provides a solution to this problem by allowing parallel transfer of a complete frame between chips. Moreover, it directly provides a parallel optical input for the classification/recognition stage of the system. The input to this next stage is, in general, a combination of features, extracted images which are then compared against the stored data to provide a match between the input and stored patterns and hence recognize the input. In the simplest case, this next stage can be an optical correlator. Other more complex possibilities include optoelectronic implementations of a multi-layer neural network architecture or a hierarchical rule-based classification tree. In any case, the ability to optically transfer the results of the pre-processing stage to the classification stage in parallel, eliminates a crucial bottleneck. In the invention disclosed herein, an opto-electronic chip capable of electronically performing morphological operations to extract features from images, remove noise and have an optical, parallel input/output capability is disclosed. This chip may then be used as an input stage to an optical, binary phase-only correlator.
Morphological operators are a class of operators which have become increasingly important in image processing and vision systems. Binary, morphological operations, such as dilation and erosion, are useful in extracting image features, removing noise and edge enhancement. These techniques have been utilized in vision applications such as object recognition, image segmentation and industrial inspection.
Morphological operations can be considered to be the application of local neighborhood operations on images. As an example, it has been demonstrated that the binary erosion and dilation operators can be implemented by convolving the input image with an image consisting of the structuring element and thresholding the resultant image. This structuring element is typically much smaller than the input image, usually a 3.times.3 or 5.times.5 pixel object. The small size of the structuring element makes electronics a very efficient method of implementing the morphological operations. Typically, the computation of the output at a single pixel will require connections only with its nearest neighbors. As a result, the implementation of morphological operations is not limited by the connectivity bottleneck that plagues more complex imaging processing algorithms, such as image correlation. The major bottleneck in most image processing systems is the communications between each level of processing. Electronic systems typically perform the pre-processing, store the information and serially transmit the information to the processing stage. Through use of optics and its parallel input/output capabilities, the present invention, as will be seen hereinafter, bypasses this crucial bottleneck.